Unsupervised learning is a type of machine learning where the algorithm learns from unlabeled data. This tutorial will guide you through the basics of unsupervised learning, including common algorithms and their applications.

Common Unsupervised Learning Algorithms

  • Clustering: Grouping data into clusters based on similarity.
    • K-Means: A simple and popular clustering algorithm.
    • Hierarchical Clustering: A more flexible clustering method.
  • Dimensionality Reduction: Reducing the number of features in the data.
    • Principal Component Analysis (PCA): A common technique for dimensionality reduction.
    • t-SNE: A technique for visualizing high-dimensional data.
  • Anomaly Detection: Identifying unusual patterns in data.
    • Isolation Forest: An algorithm that isolates anomalies instead of profiling normal data points.
    • One-Class SVM: A method for learning the boundary of a set of data points.

Application Examples

  • Market Segmentation: Grouping customers into segments based on their purchasing behavior.
  • Recommendation Systems: Suggesting items to users based on their preferences.
  • Image Processing: Enhancing images by removing noise or compressing data.

Clustering Example

For more information on unsupervised learning, check out our Advanced Unsupervised Learning Techniques.